1. Field of the Invention
The present invention relates in general to the field of information processing, and more specifically to a system and method for generating normalized data models.
2. Description of the Related Art
The use of networked data processing systems to market and sell products continues to grow. Such systems are sometimes referred to as electronic commerce systems. When an e-commerce system offers many products and many variations of the same product, effectively and efficiently guiding a user to a product that best matches the user's interest poses a complicated problem.
Data models have been developed that describe products in terms of feature families, features of families, and feature attribute values. Configurable products can be described by sets of selectable features that make up the product. A feature represents an option that can be ordered on a product. For convenience, selectable features are generally grouped by families. Families are typically classified as groups of features with the same functional purpose. Example families for an automobile are “version,” “trim package,” “exterior package,” “drives,” “engines,” “series,” “tires,” “markets,” “wheels,” “seats,” and “transmissions.” An example feature from the engines family is a “4.5 liter V8.” Features relate to each other via configuration rules.
Many Internet sites allow users to configure products by selecting a product, selecting a family, and then selecting attribute values for available features. The families and features are product specific. Thus, most configuration engines access a product specific configuration data model.
However, contextual product configuration systems allow a user to enter contextual configuration parameters that span across products. The Miller Application describes example embodiments of such a contextual product configuration display system. Product specific configuration data models often contain unique feature family, feature, and attribute references that impair the ability of a configuration engine to create configuration contexts that span across products. For example, a Product A configuration model may contain two possible engines, the 3.5 L V6 PowerTech and the 2.9 L V6 CruiseTech. A Product B configuration model may also contain two possible engines, the 4.8 L V8 HyperDrive and the 2.8 L V6 EfficiencyDrive. The nomenclature for all the engines is different, thus, making an automated contextual configuration very difficult.